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. Author manuscript; available in PMC: 2015 Feb 7.
Published in final edited form as: Psychiatr Genet. 2014 Dec;24(6):241–248. doi: 10.1097/YPG.0000000000000052

MACROD2 gene associated with autistic-like traits in a general population sample

Rachel M Jones a,b,#, Gemma Cadby a,#, John Blangero e, Lawrence J Abraham a,c, Andrew JO Whitehouse d, Eric K Moses a
PMCID: PMC4320645  NIHMSID: NIHMS660588  PMID: 25360606

Abstract

There is now substantial evidence that autistic-like traits in the general population lie on a continuum, with clinical autism spectrum disorders (ASD) representing the extreme end of this distribution. In this study, we sought to evaluate five independently identified genetic associations with ASD with autistic-like traits in the general population. In the study cohort, clinical phenotype and genomewide association genotype data were obtained from the Western Australian Pregnancy Cohort (Raine) Study. The outcome measure used was the Autism Spectrum Quotient (AQ), a quantitative measure of autistic-like traits of individuals in the cohort. Total AQ scores were calculated for each individual, as well as scores for three subscales. Five candidate single nucleotide polymorphism (SNP) associations with ASD, reported in previously published genomewide association studies, were selected using a nominal cutoff value of P less than 1.0 × 10−5. We tested whether these five SNPs were associated with total AQ and the subscales, after adjustment for possible confounders. SNP rs4141463 located in the macro domain containing 2 (MACROD2) gene was significantly associated with the Communication/Mindreading subscale. No other SNP was significantly associated with total AQ or the subscales. The MACROD2 gene is a strong positional candidate risk factor for autistic-like traits in the general population.

Keywords: association, autistic-like traits, MACROD2, Raine study, single nucleotide polymorphism

Introduction

Autism spectrum disorders (ASD) represent a group of neurodevelopmental conditions characterized by impairments in social interaction and communication, and repetitive interests and behaviors. A recent review estimated the median global prevalence of ASD at 62 cases per 10 000 children (Elsabbagh et al., 2012). The overall heritability of ASD is estimated at 90%, which is among the highest for any neuropsychiatric disorder (Hollander et al., 2003; Skuse et al., 2005). Evidence suggests that ASD have a complex inheritance in which different, likely overlapping, groups of genetic variants may cause susceptibility to disease (Veenstra-Vanderweele et al., 2004). Increasing numbers of epidemiological and genetic studies have deepened the understanding of the genetic contribution to ASD, and have shown that a variety of genetic mechanisms may be involved in the etiology (Li et al., 2012).

Autistic-like traits are subthreshold deficits in socialization and communication as well as restricted interests that do not meet the formal criteria for ASD (Constantino and Todd, 2003). Several authors have suggested that ASD can be conceptualized as the conditions arising in individuals found at the extreme end of a normal distribution of autistic-like traits (Gillberg, 1992; Constantino and Todd, 2003; Ronald et al., 2005). Population-based studies have supported this by finding that in addition to individuals with ASD, many others exhibit subthreshold autistic or autistic-like traits (Constantino and Todd, 2003; Posserud et al., 2006; Lundström et al., 2012). Subthreshold autistic-like traits have been examined in general population twin studies, with heritability estimates ranging from 36 to 87% in these studies (Ronald and Hoekstra, 2011).

With the discovery of common genetic variants associated with ASD, the question is raised whether common risk loci also contribute to variation in phenotypes such as autistic-like traits (St Pourcain et al., 2010). One methodological approach that is gaining influence in ASD research is the examination of the quantitative distribution of autistic-like traits in the broader community, rather than in a dichotomy of ASD patients and controls. Many authors have postulated that common genetic variants that are present in a significant proportion of the general population may play a role in the etiology of ASD (Campbell et al., 2006; Alarcón et al., 2008; Chakrabarti et al., 2009; Wang et al., 2009; Anney et al., 2010; Ronald et al., 2010). Further, it is thought that understanding the etiology of individual differences in autistic-like traits in the general population will help understand the causes of ASD (Ronald and Hoekstra, 2011). Between 1995 and 2008, nearly 200 genes were investigated for associations with ASD, and more than 80 of these were reported with nominally positive results (Holt et al., 2010). The Simons Foundation Autism Research Initiative (SFARI) has developed a publicly available database for ASD research, SFARI Gene, centered on genes implicated in ASD susceptibility (Banerjee-Basu and Packer, 2010). SFARI Gene serves as a comprehensive reference for all known human genes associated with ASD, currently listing 423 genes from 584 studies, as of September 2012 (Banerjee-Basu and Packer, 2010).

Despite numerous studies and reported associations, no gene has been unambiguously shown to contribute to ASD susceptibility, and independent groups have not been able to consistently replicate any associations (Yang and Gill, 2007; Abrahams and Geschwind, 2008; Holt et al., 2010).

The aim of the current study was to investigate associations between autistic-like traits in a Western Australian population sample and five SNPs previously reported to be associated with ASD.

Materials and methods

Study sample population

Data were obtained from the Western Australian Pregnancy Cohort (Raine) Study, a longitudinal investigation of pregnant women and their offspring, who were recruited from King Edward Memorial Hospital, Perth, Western Australia, or nearby private practices between 1989 and 1991 (Newnham et al., 1993). Of the 2900 pregnancies recruited into the Raine Study, 2868 live-born children have been under follow-up since the commencement of the study. The final sample consisted of 965 individuals from the Raine Study with both genotype and outcome measures. Participant recruitment from the study families was approved by the Human Ethics Committee at King Edward Memorial Hospital. Ethical approval for the 20-year follow-up was received from the Human Research Ethics Committee at the University of Western Australia. Participants provided written informed consent for data collection on autistic-like trait outcomes at ~ 20 years of age.

Measure of autistic-like traits

At the 20-year follow-up, Raine Study participants who did not have a diagnosis of intellectual disability or ASD were asked to complete the Autism Spectrum Quotient (AQ; Baron-Cohen et al., 2001). The AQ is a self-report questionnaire that provides a quantitative measure of autistic-like traits in the general population (Baron-Cohen et al., 2001). Individuals are provided with 50 statements and asked to indicate on a 4-point scale how well each statement applies to them (strongly agree, agree, disagree, strongly disagree). The items were scored on a scale ranging from 1 to 4, on the basis of previous research, according to which this scoring method retains more information about responses than does dichotomous scoring that was first proposed for this instrument (Baron-Cohen et al., 2001; Austin, 2005; Stewart and Austin, 2009; Russell-Smith et al., 2011). Scores for each item are summed to provide the total AQ, with higher scores indicating greater autistic-like traits. The total AQ is known to have good test–retest reliability (r = 0.7), and validation studies have found that scores in the general population follow a normal quantitative distribution (Baron-Cohen et al., 2001; Whitehouse et al., 2011). Factor analyses of the AQ in several countries have consistently identified three clear factors, related to social ability, attention to detail/patterns, and the understanding of others. In the current study, we divided items into the subscales identified in a study of Western Australian adults (Russell-Smith et al., 2011) who were highly similar to the sample under investigation here: Social Skills, Details/Patterns, and Communication/Mindreading. There is minimal difference between the items in these subscales and those reported in other factor analyses. For the current data set, internal reliability of the scales ranged from moderate (Communication/Mindreading: α = 0.63) to good (Details/Patterns: α = 0.78) and excellent (Social Skills: α = 0.85). In this study, total AQ scores and the scores of the three subscales were used as four continuous outcome measures.

Selection of candidate single nucleotide polymorphisms

Positional candidate SNPs were identified by conducting a search in the catalog of published genomewide association (GWA) studies by the National Human Genome Research Institute (2012). The selection was based on a nominated cutoff P-value of less than 1.0 × 10−5, which has been used by the National Human Genome Research Institute for the identification and archiving of putative associations (National Human Genome Research Institute, 2012). The search keywords included ‘autism’ as the disease/trait, and a P-value threshold less than 1.0 × 10−5. These search terms together yielded five SNPs: rs4141463 in macro domain containing 2 (MACROD2), rs7142002 in protein phosphatase 2, regulatory subunit B’ gamma (PPP2R5C), rs10513025 flanking sema domain, seven thrombospondin repeats (SEMA5A), and taste receptor, type 2, member 1 (TAS2R1), and rs10038113 and rs307059, both flanking cadherin 10, type 2 (CDH10) and cadherin 9, type 2 (CDH9). A summary of the candidate SNPs selected for the current study and their previous association details are presented in Table 1.

Table 1. Summary table of candidate SNPs (genomewide association studies; search conducted April 2012).

References Initial sample size (source) Replication sample size (source) SNP (gene)a Effect
allele		 P-value Odds ratio (95% CI;
where included)		 Platform (No. of SNPs)
Wang et al. (2009) 3101 family members, 1204 cases,
 6491 controls (AGRE, ACC)		 1390 family members, 108 cases, 540
 controls (CAP, CART)		 rs4307059 (CDH10/CDH9) C 2 × 10−10 0.84 Illumina HumanHap550
(474 019)
Ma et al. (2009) 1390 family members (CAP) 2390 family members (AGRE) rs100038113 (CHD10/CHD9) C 3 × 10−6 1.33 (1.11-1.43) Illumina 1M Infinium (775
311)
Weiss and Arking
 (2009)		 1031 families with 1553 affected
 offspring (AGRE, NIMH)		 2073 trios (NIMH, Montreal, EDSP, MGH-
 Finnish, CHB, HMCA, AGP, FAFS, ITS)		 rs10513025
 (SEMA5A/TAS2R1)		 C 1.7 × 10−6 0.55 Affymetrix 5.0 (365 000)
Anney et al. (2010) 1385 affected children from 1369
 families (AGRE, SAGE control
 cohort)		 1086 affected children from 595 families.
 1965 European, African American and
 other controls		 rs4141463 (MACROD2) T 3.7 × 10−8 0.73 (0.66-0.82) Illumina 1M Infinium (842
348)
rs7142002 (PPP2R5C) C 3 × 10−6 0.64 (0.53-0.78)
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ACC, Autism Case–Control Cohort; AGP, Autism Genome Project; AGRE, Autism Genetic Resource Exchange; CAP, Collaborative Autism Project; CART, Center for Autism Research and Treatment; CHB, Children’s Hospital Boston; EDSP, Santangelo Early Development Stages of Psychopathology Study; FAFS, Finnish Autism Family Samples; HMCA, Homozygosity Mapping Collaborative for Autism; ITS, Iranian Trio Samples; MGH-Finnish, Massachusetts General Hospital and Finnish Planmed Oy collaboration; NIMH, National Institute of Mental Health, Autism Genetics Initiative; SAGE, Study on Addiction: Genetics and Environment; SNP, single nucleotide polymorphism; TEDS, Twins Early Development Study.

a

For intergenic SNPs, 5′/3′ flanking genes listed.

Study sample

In the Raine Study, DNA was collected from blood samples from 74% of the adolescents who attended the 14-year follow-up, and a further 5% who attended the 16-year follow-up, using standardized procedures. Candidate SNP data for this study were obtained from genomewide genotype data described previously (Jones et al., 2013). Briefly, genotyping was performed on the Illumina Human 660W Quad Array (Illumina, San Diego, California, USA) and individuals were excluded due to low genotyping success (>3% missing), excessive heterozygosity, relation with another sample (identity by descent > 0.1875), ambiguous sex, and mislabeling. There were 1494 individuals whose DNA samples passed the quality control criteria and were eligible for genetic analyses, and 965 of them had completed the AQ.

Statistical methods

Generalized linear models were used to model the association of genotype and multiple covariates with autistic-like trait outcomes. Individuals included in the final analyses were those who had both genotype and autistic-like trait outcome measures, although not all participants had data for all covariates.

Covariate selection

Potential covariates that may plausibly influence AQ scores were selected. These included sex, season of birth (summer, autumn, winter, or spring), family income (family earning > or < $24 000 at the time of pregnancy), maternal education (mother completed secondary school or did not complete secondary school), gestational age (gestation < 32 weeks, 32–37 weeks, 38–40 weeks, or > 40 weeks), father’s BMI before pregnancy, mother’s BMI before pregnancy, father’s age at conception, mother’s age at conception, and age at AQ completion.

Final models for total AQ scores and each subscale were constructed using a stepwise variable selection procedure, to identify independent predictors of each outcome from potential covariates. A P-value of less than 0.05 was used to select covariates to be retained for multiple linear regression modeling. Each model started with all potential covariates, and then the least significant variables were removed one at a time. Once a model containing only significant variables was reached, previously omitted variables were added back in to check for changes in significance.

Genetic analysis

Imputed dose scores (imputed to HapMap Phase II CEU) were used for analyses. Dose scores represent the posterior probability of the minor allele and can take any value between 0 and 2. Hardy–Weinberg equilibrium (HWE) was calculated using Fisher’s exact test. Genetic association tests were performed using multiple linear regression, with each SNP added separately to the full models and the SNP P-value considered for significance. Directional hypotheses were used for each SNP, with an association defined as significant when the direction of the effect of the SNP was in the same direction as in previous studies. Confidence intervals (95% CIs) were calculated for the effect of any significantly associated SNP. Adjustments for multiple testing were made using a Bonferroni correction, with SNP association results considered significant if the P-value was below 0.01.

Results

Power

Power was calculated using Quanto (Gauderman and Morrison, 2007). For a common variant with a minor allele frequency (MAF) of 0.3, we had greater than 80% power to detect a 1 U change in the three subscales: Social Skills, Details/Patterns and Communication Mindreading. For a variant with an MAF of 0.3, we had 40% power to detect a 1 U change in the total AQ.

Descriptive statistics

Characteristics of the current study sample are listed in Table 2. Just over half (51.3%) of the sample were female, and the mean age at AQ completion was 19.68 years (SD = 0.70). The mean total AQ score was 103.2 (SD = 12.60). The total AQ and each subscale were normally distributed.

Table 2. Characteristics of Raine study participants with AQ and GWA data available (n = 965).

Continuous n Mean (SD)
Maternal age at conception (years) 965 28.66 (5.65)
Paternal age at conception (years) 646 31.68 (6.18)
Maternal BMI at conception 956 22.25 (4.00)
Paternal BMI at conception 827 24.44 (3.28)
Age at AQ completion (years) 965 19.68 (0.70)
Total AQ score 965 103.2 (12.60)
Categorical n n(%)
Season of birth 965
 Summer 301 (31.2)
 Autumn 222 (23.0)
 Winter 196 (20.3)
 Spring 246 (25.5)
Sex 965
 Male 470 (48.7)
 Female 495 (51.3)
Gestational age 955
 < 32 weeks 13 (1.4)
 32-37 weeks 157 (16.4)
 38-40 weeks 624 (65.3)
 > 40 weeks 161 (16.9)
Family income 937
 Income < $24 000 293 (31.3)
 Income > $24 000 644 (68.7)
Maternal education 956
 Secondary school not completed 501 (52.4)
 Secondary school completed 455 (47.6)
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AQ, Autism Spectrum Quotient; GWA, genomewide association.

Consistency of genotype distributions with HWE were examined for each candidate SNP using Fisher’s exact test. Table 3 lists the genotypic distributions, MAFs, and HWE P-values. Results indicate that all SNPs were consistent with HWE (all P’s > 0.05).

Table 3. Genotype and minor allele frequencies.

SNP Genea Major homozygote
 Heterozygote
 Minor homozygote
 HWE P-value MAF
Gen n (%) Gen n (%) Gen n (%)
rs10513025 SEMA5A/TASR1 TT 872 (94) CT 55 (6) CC 0 (0) 1.00 2.97
rs10038113 CDH10/CDH9 TT 344 (36) CT 455 (48) CC 154 (16) 0.89 40.03
rs4307059 CDH10/CDH9 TT 384 (40) CT 427 (44) CC 154 (16) 0.06 38.62
rs7142002 PPP2R5C TT 841 (88) CT 115 (12) CC 3 (< 1) 1.00 6.31
rs4141463 MACROD2 CC 325 (34) TC 459 (49) TT 160 (17) 0.95 41.26
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Gen, genotype; HWE, Hardy–Weinberg equilibrium; MAF, minor allele frequency; SNP, single nucleotide polymorphism.

a

For intergenic SNPs, 5′/3′ flanking genes listed.

Candidate single nucleotide polymorphism analysis

Covariate modeling

Models for all potential covariates were constructed. The final models for total AQ, Social Skills, Details/Patterns, and Communication/Mindreading are presented in Table 4.

Table 4. Models for potential covariates.
Variable Total AQ
 Social skills
 Details/Patterns
 Communication/Mindreading
Coefficient SE P-value Coefficient SE P-value Coefficient SE P-value Coefficient SE P-value
Sexa 51.539 22.961 0.025 0.997 0.395 0.012 1.503 0.366 < 0.001
Age at AQ completion 2.706 0.803 < 0.001 0.430 0.190 0.024
Season of birthb
 Summer 0.528 0.365 0.149
 Autumn 0.831 0.387 0.032
 Spring 1.008 0.374 0.007
Mother’s BMI 0.263 0.100 0.009 0.106 0.049 0.031 0.108 0.047 0.024 0.096 0.034 0.004
Father’s age at conception −0.066 0.030 0.027 −0.052 0.021 0.014
Mother’s age at conception 0.083 0.037 0.026
Family incomec 2.541 0.889 0.004 1.129 0.453 0.007
Maternal educationd −2.685 0.826 0.001 −0.794 0.367 0.031
Age × Sex −2.446 1.166 0.036
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AQ, Autism Spectrum Quotient.

a

Reference level set to female.

b

Reference season set to winter.

c

Reference level set to income greater than $24 000.

d

Reference level set to mother did not complete secondary school.

Male sex, increased age at AQ completion, higher mother’s BMI, and greater family income were associated with a higher total AQ score. Maternal education was significantly inversely associated with total AQ scores.

Male sex was associated with higher Social Skill scores, indicating poorer social skills, as was mother’s BMI and greater family income. A higher maternal age at conception was also associated with higher Social Skill scores.

Male sex and mother’s BMI were significantly associated with Details/Patterns. Father’s age at conception and maternal education were both inversely associated with Detail/Pattern scores.

Higher Communication/Mindreading scores were associated with increased age at AQ completion and mother’s BMI. Increased father’s age at conception was associated with lower Communication/Mindreading scores. Season of birth was also a significant covariate, spring and autumn births being associated with increased Communication/Mindreading scores compared with winter birth.

Single nucleotide polymorphism analysis

Genetic association tests were performed with multiple linear regression. Each SNP was added to the covariate model for total AQ, Social Skills, Details/Patterns, and Communication/Mindreading. Tables 5-8 contain results from the analysis of candidate SNPs, after adjusting for covariates. The results in Table 8 show a significant association between SNP rs4141463 (MACROD2 gene) and Communication/Mindreading (P = 0.006). Each additional copy of the effect allele, T, decreased an individual’s Communication/Mindreading by 0.475 U (95% CI = −0.840 to −0.110). The effect allele T is in the same direction as in the initial report (Anney et al., 2010).

Table 5. Multiple linear regression analysis for total AQ*.
SNP Gene Effect allele Coefficient SE P-value
rs10513025 SEMA5A, TAS2R1 C 1.931 1.710 0.87
rs10038113 Intergenic C −0.070 0.580 0.548
rs4307059 CDH10, CDH9 C 1.085 0.565 0.9725
rs7142002 PPP2R5C C 0.354 1.164 0.6195
rs4141463 MACROD2 T −0.948 0.583 0.052
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AQ, Autism Spectrum Quotient.

*

Adjusted for sex, age at AQ completion, mother’s BMI, family income, and maternal education.

Table 8. Multiple linear regression analysis for communication/mindreading*.
SNP Gene Effect allele Coefficient SE P-value
rs10513025 SEMA5A, TAS2R1 C 0.571 0.558 0.846
rs10038113 Intergenic C 0.065 0.189 0.365
rs4307059 CDH10, CDH9 C −0.224 0.183 0.111
rs7142002 PPP2R5C C −0.337 0.380 0.188
rs4141463 MACROD2 T −0.475 0.186 0.006
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AQ, Autism Spectrum Quotient.

*

Adjusted for age at AQ completion, season of birth, mother’s BMI, and father’s age at conception.

Discussion

In this study, we report an association between the rs4141463 SNP that resides within intron 5 of the MACROD2 gene on chromosome 20p12 and autistic-like traits in a Western Australian population. This SNP was previously reported to be associated with ASD by the Autism Genome Project Consortium (Anney et al., 2010). In both studies, the minor allele T was associated with a decrease in autistic traits.

The candidate SNP rs4141463 is located on chromosome 20p12.1. This SNP resides within an intron of the MACROD2 gene. The function of the MACROD2 protein is largely unknown, but it contains a macro domain, which is a high-affinity ADP-ribose-binding domain, and is important for multiple biological processes (Mouren et al., 2012). Copy number variants in MACROD2 have been reported in schizophrenia (Xu et al., 2009). There is also evidence that suggests that this SNP, as well as others in the region, could act to regulate the expression of the phospholipase D2 (PLD2) gene (Anney et al., 2010). Phospholipase proteins could play an important role in risk for ASD. The protein derived from PLD2 has been shown to regulate axonal growth (Kanaho et al., 2009) and metabotropic receptor signaling (Dhami and Ferguson, 2006).

No significant associations were found between autistic-like traits and the remaining four SNPs. SNP rs7142002 is located within an intron of the protein phosphatase 2, regulatory subunit B’, gamma (PPP2R5C) gene at 14q32.31, and its association with autism was reported by the Autism Genome Project Consortium (Anney et al., 2010), with the C allele protective for ASD.

SNP rs10513025 is located between the genes sema domain, seven thrombospondin repeats (SEMA5A), and taste receptor, type 2, member 1 (TAS2R1) on chromosome 5p15.2 in a region of high linkage disequilibrium. In an earlier linkage and genomewide association study, the C allele was associated with a decreased risk for autism (Weiss et al., 2009).

The SNP rs4307059, located in the 5p14.1 region, is in a linkage disequilibrium block in an intergenic region between the cadherin 10, type 2 (CDH10) and cadherin 9, type 2 (CDH9) genes. The GWA study in 2009 found an association between this SNP and ASD, with the C allele reducing the risk for ASD (Wang et al., 2009). A study in 2010 successfully replicated the association between rs4307059 and social communication spectrum phenotypes in the general population (St Pourcain et al., 2010).

SNP rs10038113 is also located within an intergenic region between the CDH10 and CDH9 genes on chromosome 5p14.1. A GWA study reported an association between ASD and SNP rs10038113, with the C allele conferring an increased risk for ASD (Ma et al., 2009). It is likely that one or more nearby functional variants are responsible for the signal in this area, as rs10038113 does not reside within known genes or regulatory sequences (Ma et al., 2009). In the surrounding region, there are numerous segments that exhibit a high degree of evolutionary conservation, suggesting potential regulatory functions that have yet to be determined (Ma et al., 2009).

MACROD2 as a positional candidate susceptibility gene

The function of the protein encoded by MACROD2 is poorly understood. The protein contains a macro domain that is evolutionarily conserved and reaches its highest expression in the adult and fetal human brain (Debette et al., 2010). Macro domains act as deacetylases that bind ADP-ribose, a signaling molecule generated by the deacetylation of acetylated lysines in histones and other proteins, and have an important role in multiple biological processes including DNA repair, transcriptional activation, and repression (Debette et al., 2010; Mouren et al., 2012). The rs4141463 SNP is located within a hypersensitive site in a MACROD2 intron, which may indicate an open chromatin conformation and thus may serve a regulatory role in the expression of MACROD2. However, this variant may not be the actual causal variant having an impact on ASD risk as GWA localizations are based on linkage disequilibrium, implying that the actual causal variant could possibly be located some distance away on the same chromosome.

Other studies have reported copy number variants at MACROD2 in association with schizophrenia (Xu et al., 2009), brain infarct (Debette et al., 2010), and brain volume in multiple sclerosis (Baranzini et al., 2009). A deletion in the MACROD2 gene has also been reported in attention deficit hyperactivity disorder (ADHD; Lionel et al., 2011). In addition to the copy number variants in the MACROD2 gene, other rare variants have also been found to overlap between ADHD and ASD studies (Lionel et al., 2011). ADHD is known to be comorbid in ~ 78% of children with ASD (Lee and Ousley, 2006). Furthermore, twin studies have identified that close to three-quarters of the genetic risk factors for ADHD are shared with ASD (Kendler, 2010).

The apparent overlap of copy number variants between ADHD and ASD and other neurologic and psychiatric disorders supports evidence that variants in a common set of genes could be involved in the etiology of several neuropsychiatric disorders (Carroll and Owen, 2009; Guilmatre et al., 2009; Saus et al., 2010). This implies that there may be specific genetic loci or alleles that increase an individual’s risk for developing any of these neuropsychiatric disorders, and that these loci may represent a common biological pathway (Carroll and Owen, 2009). The results from this study add further interest to the region on chromosome 20p12.1 as a plausible candidate region for ASD etiology. These results also support evidence that a genetic variant initially detected in individuals with clinically diagnosed ASD has an impact on autistic-like traits in the general population.

The current finding needs to be interpreted in the context of two failed replications of the rs4141463 SNP in probands with ASD. Curran et al. (2011) attempted to replicate the association using an independent case–control design with 1170 European cases and 35 307 controls and Prandini et al. (2012) sought to replicate genetic markers from recent genomewide and candidate-gene studies in the Italian Autism Network cohort, consisting of 233 probands, 423 parents, and 90 siblings. In both studies, cases met the Diagnostic and Statistical Manual of Mental Disorders, 4th ed. criteria for Autism, Asperger’s syndrome, or pervasive developmental disorder not otherwise specified on the basis of the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule. Both studies failed to observe a significant association between the rs4141463 SNP and ASD, with both P-values greater than 0.05 (Curran and colleagues: P = 0.50, odds ratio = 0.99, 95% CI = 0.88–1.11; Prandini and colleagues: P > 0.05).

In this study, we have reported an association between rs4141463 in MACROD2 and the Communication/Mindreading subscale in a general population of young Western Australian adults; however, we did not find associations for four other SNPs also previously reported to be associated with ASD. The failure to replicate a genetic association is a commonly observed phenomenon in complex human disease genetics research, including ASD research, and there are a host of potential reasons for this, including a false-positive finding in the original study, ethnic heterogeneity between data sets, environmental interactions, age-dependent effects, epistasis, and inadequate statistical power (Chanock et al., 2007; Shriner et al., 2007; Greene et al., 2009). Further genetic characterization of the rs4141463 SNP association is warranted, but is beyond the scope of this study, and may possibly require exhaustive DNA sequencing to identify all variants within the MACROD2 gene region followed by a variety of detailed molecular biological analyses, like those we have recently described (Karimi et al., 2009; Kaskow et al., 2014).

Table 6. Multiple linear regression analysis for social skills*.

SNP Gene Effect allele Coefficient SE P-value
rs10513025 SEMA5A, TAS2R1 C 0.930 0.838 0.866
rs10038113 Intergenic C 0.011 0.285 0.485
rs4307059 CDH10, CDH9 C 0.436 0.277 0.942
rs7142002 PPP2R5C C 0.348 0.572 0.728
rs4141463 MACROD2 T −0.179 0.286 0.267
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*

Adjusted for sex, mother’s BMI, mother’s age at conception and family income.

Table 7. Multiple linear regression analysis for details/patterns*.

SNP Gene Effect allele Coefficient SE P-value
rs10513025 SEMA5A, TAS2R1 C 0.314 0.787 0.655
rs10038113 Intergenic C −0.044 0.267 0.665
rs4307059 CDH10, CDH9 C 0.243 0.258 0.826
rs7142002 PPP2R5C C −0.332 0.536 0.268
rs4141463 MACROD2 T −0.256 0.264 0.166
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*

Adjusted for sex, mother’s BMI, father’s age at conception and maternal education.

Acknowledgements

Grant sponsor: National Health and Medical Research Council; Grant number: 572613. A.J.O.W. was supported by a Career Development Fellowship from the NHMRC (#1004065). The authors would like to acknowledge the National Health and Medical Research Council (NHMRC) for their long-term contribution toward funding the study over the last 20 years. Core Management of the Raine Study has been funded by the University of Western Australia (UWA), Curtin University, the UWA Faculty of Medicine, Dentistry and Health Sciences, the Raine Medical Research Foundation, the Telethon Institute for Child Health Research, and the Women’s and Infants Research Foundation. This study was partly funded by NHMRC Project Grant #572613. The authors are extremely grateful to all of the families who took part in this study and the whole Raine Study team, which includes the Cohort Manager, Data Manager, and data collection team.

Footnotes

Conflicts of interest

There are no conflicts of interest.

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